Blockchain Audit Trails Resolve the Electronic Health Record Traceability Problem Created by Generative AI

Authors

  • Thomas F Heston Department of Family Medicine, University of Washington, Seattle, USA; Department of Medical Education and Clinical Sciences, Elson S. Floyd College of Medicine, Washington State University, Spokane, USA https://orcid.org/0000-0002-5655-2512

DOI:

https://doi.org/10.5281/zenodo.20337356

Keywords:

electronic health records, generative AI, blockchain, audit trail, traceability, large language models, clinical governance, provenance

Abstract

As large language models generate clinical documentation at scale, electronic health records increasingly contain AI-produced content with no verifiable provenance. The field has named this traceability gap but has not yet specified an architectural solution. Blockchain-based audit logging — append-only, cryptographically chained, and tamper-resistant — provides the answer: a lightweight layer capturing model identity, prompt hash, output fingerprint, and timestamp at generation, creating a verifiable chain of custody before text enters the clinical record. Adopting blockchain audit logging as a standard condition of institutional large language model deployment would resolve this traceability crisis before it becomes irreversible.

References

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Published

2026-05-22

How to Cite

Heston, T. F. (2026). Blockchain Audit Trails Resolve the Electronic Health Record Traceability Problem Created by Generative AI. Internet Medical Journal, 1(1), e20337356. https://doi.org/10.5281/zenodo.20337356

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